Main Article Content

Abstract

The academic success of scholarship recipients is not only determined by their academic abilities but also by various non-academic factors that are often difficult to measure objectively. The large number of interrelated indicators presents a challenge in identifying the most dominant factors, especially when these variables exhibit high correlations and lead to data redundancy. This study aims to identify the dominant non-academic factors influencing the academic success of scholarship recipients using Principal Component Analysis (PCA). A total of 262 students completed a questionnaire consisting of 54 non-academic items that had previously undergone validity and reliability testing. PCA was employed to reduce data dimensionality and produced 38 principal components with a cumulative explained variance of 95.08%, indicating effective dimensionality reduction without significant loss of information. The loading matrix analysis revealed that psychological conditions, learning methods, major suitability, learning motivation, and financial conditions were the most dominant contributors to the principal components. These findings provide a more structured understanding of the non-academic factors that should be considered in the development and monitoring of scholarship programs.

Keywords

Academic Success Factors Factor Analysis Principal Component Analysis Scholarship

Article Details

Author Biographies

Nur Mufidah, Politeknik Caltex Riau

Teknologi Informasi Politeknik Caltex Riau

Dadang Syarif Sihabudin Sahid, Politeknik Caltex Riau

Teknologi Informasi Politeknik Caltex Riau

Satria Perdana Arifin, Politeknik Caltex Riau

Teknologi Informasi Politeknik Caltex Riau
How to Cite
Mufidah, N., Syarif Sihabudin Sahid, D., Perdana Arifin, S., & Ari Sandi, W. (2025). ANALISIS FAKTOR DOMINAN KEBERHASILAN AKADEMIK MAHASISWA PENERIMA BEASISWA MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS. Jurnal Komputer Terapan, 11(2), 98–106. https://doi.org/10.35143/jkt.v11i2.6819

References

  1. Darsi and Rasmita, “Prestasi Belajar Mahasiswa ditinjau dari Dimensi Regulitas dalam Kurikulum Merdeka,” Indones. Inst. Corp. Learn. Stud., vol. 5, no. September, p. 5, 2024.
  2. G. Fithria, S. Amanah, and M. Simanjuntak, “Faktor-Faktor yang Memengaruhi Keberhasilan Studi Penerima Bidikmisi di Universitas Sultan Ageng Tirtayasa,” J. Apl. Bisnis dan Manaj., vol. 8, no. 3, pp. 833–845, 2022, doi: 10.17358/jabm.8.3.833.
  3. Suripto and A. S. Rahmanita, Rr Nurul Kirana, “Teknik pre-processing dan classification dalam data science,” Master of Industrial Engineering BINUS. [Online]. Available: https://mie.binus.ac.id/2022/08/26/teknik-pre-processing-dan-classification-dalam-data-science/
  4. Y. E. Kurniawati, “What is Exploratory Data Analysis?,” School of Information Systems BINUS. [Online]. Available: https://sis.binus.ac.id/2025/01/21/39476/
  5. N. Nurmalitasari and E. Purwanto, “Prediksi Performa Mahasiswa Menggunakan Model Regresi Logistik,” J. Deriv. J. Mat. dan Pendidik. Mat., vol. 9, no. 2, pp. 145–152, 2022, doi: 10.31316/jderivat.v9i2.2639.
  6. R. K. Putri, M. Athoillah, and A. Haqiqiyah, “Analisis Faktor yang Mempengaruhi Ketepatan Kelulusan Mahasiswa dengan Algoritma Regresi Linear,” J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 5, no. 2, pp. 671–680, 2024, doi: 10.46306/lb.v5i2.571.
  7. P. Bintoro, Ratnasari, E. Wihardjo, I. P. Putri, and A. Asari, Pengantar Machine Learning. Solok, Sumatera Barat: Pt. Mafy Media Literasi Indonesia, 2024. [Online]. Available: https://repository.um.ac.id/5619/1/fullteks.pdf
  8. S. A. Syuhada, S. H. Hasanah, and P. S. Statistika, “Analisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran di Indonesia dengan Pendekatan Principal Component Analysis (PCA) dan Analisis Profil,” vol. 2, no. 1, pp. 1134–1150, 2025.
  9. R. A. Zuama, M. A. Ghani, D. Gunawan, and A. L. Matihudin, “Implementasi Metode Waterfall Dalam Mengembangkan Sistem Informasi Ujian Online Dengan Fitur Proctoring,” INFORMATICS Educ. Prof. J. Informatics, vol. 7, no. 2, p. 218, 2023, doi: 10.51211/itbi.v7i2.2382.
  10. M. M. Sanaky, “Analisis Faktor-Faktor Keterlambatan Pada Proyek Pembangunan Gedung Asrama Man 1 Tulehu Maluku Tengah,” J. Simetrik, vol. 11, no. 1, pp. 432–439, 2021, doi: 10.31959/js.v11i1.615.
  11. M. Celestin et al., “PRINCIPAL COMPONENT ANALYSIS FOR SIMPLIFYING MULTIVARIATE FINANCIAL DATA IN,” vol. 9, no. 02, pp. 171–179, 2025.
  12. A. Hasanah, “Kesesuaian Minat Karir dengan Keputusan Memilih Jurusan di Perguruan Tinggi,” J. Classr. Action Res., vol. 5, pp. 198–202, 2023.
  13. F. Aryasatya, A. Katrina, R. F. Syabila, F. Siregar, F. Matematika, and P. Alam, “Identifikasi Faktor-Faktor Esensial dalam Hasil Evaluasi Siswa menggunakan Teknik Principal Component Analysis ( PCA ),” vol. 4, pp. 6423–6437, 2024.
  14. A. Fadila et al., “Pengaruh Motivasi Belajar terhadap Peningkatan Prestasi Akademik Siswa,” WACANA J. Bahasa, Seni dan Pengajaran, vol. 7, pp. 121–133, 2023.
  15. Filan Firmansyah, Saputra Dwi Nurchaya, and Zuhana Realita Alfy, “Implementasi Algoritma Blowfish Pada Sistem Manajemen Surat Dengan Pendekatan Rational Unified Process Yang Ramah Lingkungan,” JSiI (Jurnal Sist. Informasi), vol. 11, no. 2, pp. 1–6, 2024, doi: 10.30656/jsii.v11i2.9065.
  16. L. S. Ihzaniah, A. Setiawan, and R. W. N. Wijaya, “Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing,” Jambura J. Probab. Stat., vol. 4, no. 1, pp. 17–29, 2023, doi: 10.34312/jjps.v4i1.18948.
  17. A. Rahmadhani, D. S. Sihabudin Sahid, and Y. D. Lulu Widyasari, “Implementasi SEM-Multiple Linear Regression dalam Prediksi Jumlah Pendaftaran Mahasiswa Baru di Perguruan Tinggi XYZ,” J. Nas. Teknol. dan Sist. Inf., vol. 9, no. 2, pp. 150–162, 2023, doi: 10.25077/teknosi.v9i2.2023.150-162.
  18. N. Nurdiansyah, M. Muliadi, R. Herteno, D. Kartini, and I. Budiman, “Implementasi Metode Principal Component Analysis (PCA) dan Modified K-Nearest Neighbor pada Klasifikasi Citra Daun Tanaman Herbal,” J. Mnemon., vol. 7, no. 1, pp. 1–9, 2024, doi: 10.36040/mnemonic.v7i1.6664.
  19. H. Sariwati and I. Komputer, “Penerapan Algoritma Principal Component Analysis,” vol. 1, no. 5, pp. 1–16, 2024.